This project addresses statistical problems generated from collaboration with scientists in other program areas and general statistical problems of current interest. This project is a continuing activity of the Section on Mathematical Statistics and other members of the Branch. Papers have been submitted, are in review or were published in FY 1994 on the following statistical subjects: evaluation of proxy respondents and auxiliary information as adjustments for nonresponse or attrition in disease surveys; modeling time series for count data from a relapsing- remitting disease; modeling seasonal change in time series regression relationships; development of both discrete and continuous time Markov models for longitudinal categorical data which allow for random and different processes across subjects; derivation of statistical methods for the detection of and tests of differences among spatial disease clusters. Other work in progress includes: methods to improve coverage in surveys; estimation of time-to-event data with interval censoring; site selection for epidemiologic surveys; analysis of response surface data with spatial and temporal components; modeling of response surfaces with spatially correlated errors; application of splines to estimate model parameters of multiple correlated response surfaces; modeling effect changes of covariates in the presence of spatial correlation; analysis of bioequivalence trials with multiple, nonlinear responses to treatment; combining information from negatively correlated non-linear regressions; development of a generalized estimating equation approach for the analysis of spatially dependent binary data; application of bootstrap methods to longitudinal natural history data for the design and analysis of therapeutic trials for relapsing-remitting disease; use of variance component methods to assess the precision of biochemical measurements; using a Markov chain model to study three state disease processes; and sampling strategies for spatial point processes with multiple types of clustering.